Composite neural network load models for power system stability analysis

Ali Keyhani, Wenzhe Lu, Gerald T. Heydt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Proper load models are essential to power system stability analysis. This paper proposes a methodology for the development of neural network (NN) based composite load models for power system stability analysis. A two-step modeling procedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the distribution level. Then, the test bed data is used to develop a composite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controling an induction motor load is presented.

Original languageEnglish (US)
Title of host publication2004 IEEE PES Power Systems Conference and Exposition
Pages1159-1163
Number of pages5
StatePublished - Dec 1 2004
Event2004 IEEE PES Power Systems Conference and Exposition - New York, NY, United States
Duration: Oct 10 2004Oct 13 2004

Publication series

Name2004 IEEE PES Power Systems Conference and Exposition
Volume2

Other

Other2004 IEEE PES Power Systems Conference and Exposition
Country/TerritoryUnited States
CityNew York, NY
Period10/10/0410/13/04

Keywords

  • Artificial neural networks
  • Composite load modeling
  • Power systems
  • Stability analysis

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint

Dive into the research topics of 'Composite neural network load models for power system stability analysis'. Together they form a unique fingerprint.

Cite this